Many different learning styles exist nowadays. In general, they contain a sequence of steps the student has to go through in order to get new knowledge. The truth, however, is that the teacher most often provides students with a problem, a dozen of facts considered to be an “absolute truth”, his own solution to the problem and a subsequent assessment. Such, well known, learning pattern has a couple of shortcomings that many scientific papers are trying to solve. This paper aims to solve a general drawback of the standard learning approach, where the students accumulate knowledge while remembering set of definitions that subsequently do not know how to use, because they often do not understand them – how and why they were obtained. As an alternative approach, this paper defines a learning model where the teacher provides just a couple of “learning blocks” considered to be axiomatic set for a particular subject or a lesson, together with a number of problems that students have to solve. While solving each individual problem, the student himself reaches new definitions, which he or she then adds to his “dictionary” of “learning blocks”. In other words, the students build new, more complex, “learning blocks” by their own, based on a few core “blocks” or other “blocks”, which they already are built by their own. This learning model follows the philosophy that a theorem is composed by several axioms (core “learning blocks”) and/or one or more other theorems (newly built “learning blocks”). To demonstrate the relevance of the presented model, a specific domain was chosen, where the model is applied – Computer Science. There, the students are provided with the opportunity to study Computer Science through Computer Science itself, having just a few key “learning blocks” initially.
Learning block, Controlled self-study, Learning model, Computer Science, Algorithms, Self-generated analogies, Self-explanation
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Cite this paper
Radoslav Yoshinov, Oleg Iliev. (2017) How to learn Computer Science, using Computer Science. International Journal of Computers, 2, 223-228
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